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Feature-Based Opinion Mining
Classifying evaluative texts at the document level or the sentence level does not tell what the opinion holder likes and dislikes. A positive document on an object does not mean that the opinion holder has positive opinions on all aspects or features of the object. Likewise, a negative document does not mean that the opinion holder dislikes everything about the object. In an evaluative document (e.g., a product review), the opinion holder typically writes both positive and negative aspects of the object, although the general sentiment on the object may be positive or negative. To obtain such detailed aspects, going to the feature level is needed. Based on the model presented earlier, three key mining tasks are:
1. Identifying object features: For instance, in the sentence “The picture quality of this camera is amazing,” the object feature is “picture quality”. In [10], a supervised pattern mining method is proposed. In [6, 12], an unsupervised method is used. The technique basically finds frequent nouns and noun phrases as features, which are usually genuine features. Clearly, many information extraction techniques are also applicable, e.g., conditional random fields (CRF), hidden Markov models (HMM), and many others.
2. Determining opinion orientations: This task determines whether the opinions on the features are positive, negative or neutral. In the above sentence, the opinion on the feature “picture quality” is positive. Again, many approaches are possible. A lexicon-based approach has been shown to perform quite well in [3, 6]. The lexicon-based approach basically uses opinion words and phrases in a sentence to determine the orientation of an opinion on a feature. A relaxation labeling based approach is given in [12]. Clearly, various types of supervised learning are possible approaches as well.
3. Grouping synonyms: As the same object features can be expressed with different words or phrases, this task groups those synonyms together. Not much research has been done on this topic. See [1] for an attempt on this problem.
Mining Comparative and Superlative Sentences
Directly expressing positive or negative opinions on an object or its features is only one form of evaluation. Comparing the object with some other similar objects is another. Comparisons are related to but are also different from direct opinions. For example, a typical opinion sentence is “The picture quality of camera x is great.” A typical comparison sentence is “The picture quality of camera x is better than that of camera y.” In general, a comparative sentence expresses a relation based on similarities or differences of more than one object. In English, comparisons are usually conveyed using the comparative or the superlative forms of adjectives or adverbs. The structure of a comparative normally consists of the stem of an adjective or adverb, plus the suffix -er, or the modifier “more” or “less” before the adjective or adverb. The structure of a superlative normally consists of the stem of an adjective or adverb, plus the suffix -est, or the modifier “most” or “least” before the adjective or adverb. Mining of comparative sentences basically consists of identifying what features and objects are compared and which objected are preferred by their authors (opinion holders). Details can be found in [4, 7].
KEY APPLICATIONS
Opinions are so important that whenever one needs to make a decision, one wants to hear others’ opinions. This is true for both inpiduals and organizations. The technology of opinion mining thus has a tremendous scope for practical applications.
Inpidual consumers: If an inpidual wants to purchase a product, it is useful to see a summary of opinions of existing users so that he/she can make an informed decision. This is better than reading a large number of reviews to form a mental picture of the strengths and weaknesses of the product. He/she can also compare the summaries of opinions of competing products, which is even more useful.